Following the results we showed in the first part of our series on Bollinger bands, let us show you through real-life examples how you can use this indicator to set up profitable trades. We have created two predefined scans for you to use, and we will also show how to backtest your ideas using our Colab notebook.
Key takeaways
- After breaking out of the Bollinger bands, stocks tend to have a predictable and contained movement.
- The Broken Wing Butterfly strategy allows traders to profit from small price movements while eliminating risk on one side of the trade.
- We designed two predefined scans for this strategy. You can even run your own backtest on any ticker using our Google Colab notebook.
A Quick Reminder of Our General Backtesting Results
Before we begin looking at how to trade using Bollinger Bands, let’s recap the general results of our backtesting on a variety of tickers (feel free to refer to Part 1 of this study for more information) and highlight a few key takeaways.
First of all, our analysis showed that after a breakout or breakdown from the bands, there tended to be a noticeable price movement in the following two weeks. This means that traders can potentially capitalize on these short-term price movements by opening a trade position after the Bollinger Bands have been breached.
Moreover, we observed that the 2-3 day range was ideal for analyzing Bollinger Band breakouts, as it provided a good balance between statistical significance and frequency of events.
Furthermore, our backtesting results showed that top stocks and ETFs on indexes displayed similar patterns of behavior when it came to Bollinger Band breakouts, with a quick rebound in bullish markets and a temporary recovery followed by a return to the downward trend in bearish markets. If you recall, this is how we conducted the backtest:
In the case above (an image taken directly from our Part 1 article), we saw that ETFs tend to show similar answers to periods outside the Bollinger bands in both bull (left) and bear (right) markets. To simplify, we notice an average price rebound once ETFs closed below the lower band for 2 days in a row (see red lines above), and a slowdown in the price growth for the scenario in which ETFs close above the upper band for 2 consecutive days (see green lines above).
However, ETFs focused on commodities showed a different story, with their movements being more closely tied to the overall market conditions rather than their recent breakout or breakdown from the Bollinger Bands.
In the next sections, we will teach you how to conduct your own backtesting using Bollinger Bands and provide tips on how to interpret the results and use them in your trading strategy. We will also discuss some common pitfalls to avoid when using Bollinger Bands and provide real-life examples of successful trades made based on our backtesting results.
Why We Chose a Broken Wing Butterfly Strategy
Now that we have a clear result, we can consider how to trade this idea. In this article, we suggest using a broken-wing butterfly strategy for trading Bollinger band breakouts. This choice isn’t random; it’s based on several key benefits that fit nicely with the breakout patterns we’ve observed.
The broken-wing butterfly, particularly in its debit form, allows traders to capitalize on small movements in stock price. This characteristic is crucial because our analysis shows that stocks tend to move in a predictable and contained manner after breaching the Bollinger bands.
The strategy effectively eliminates risk on one side of the trade, which is something you generally like in a highly volatile market (remember: you’re trading a stock that finds itself outside the Bollinger bands, so its volatility is higher than usual). Moreover, it offers a high potential profit ratio, often exceeding 100%, making it an attractive option for traders looking for significant returns on their investments.
Focusing on the debit form of the broken wing butterfly, we prefer it due to its high profit-to-loss ratio. It enables building a cost-effective option strategy with a directional bias, tailored to capitalize on the specific stock movements identified through our Bollinger bands analysis.
The trade setup not only comes with an impressive profit-to-loss ratio but also mitigates risk on one end. Typically, only a minor adjustment in stock price is required to hit maximum profit, especially if the strategy is structured correctly around the short strike price—which often isn’t far from the current stock price.
This approach offers a trade-off that we generally like: accepting a limited loss for a chance at a relatively high reward. You basically want the underlying security to reach the short strike price for maximum profit realization, a scenario well within reach given the contained stock movements post-Bollinger band breakouts. In essence, the broken-wing butterfly strategy provides a structured, low-risk idea to exploit the subtle (yet often predictable) shifts in stock prices following a breakout.
Of course, nothing stops you from using a different strategy, like a credit or debit spread, in the Bollinger breakout case. However, we’d personally choose the broken-wing butterfly for the reasons mentioned above.
The Two Predefined Scans We Designed
On our options screener, we’ve set up two predefined scans designed for this strategy. The first scan targets situations where a stock price has closed below the lower Bollinger band for three consecutive days, which is an opportunity to trade a riskless up broken butterfly (look for the “Riskless Up Butterfly Ouyside Bollinger Bands” predefined scan on our website).
Conversely, the second scan looks for stocks that have closed above the upper Bollinger band for three days in a row, setting up a riskless down broken butterfly (you can easily find the “Riskless Down Butterfly Outside Bollinger Bands” scan on the website).
As a general reminder, consider that these scans look for cases of stocks trading outside the bands for at least 1 day. This means that you have a choice: either you trade immediately, or wait for the stock to stay outside the bands for another day or two before opening a trade.
Note that, if you look at the filters of each scan, we’re not introducing any limit in terms of a trade’s expected value. This is because we do not want to limit your choice on the implied probability of profit given by an option price in a scenario where you can run your own backtests. Ideally, you’ll check these scans every day and run a very quick backtest (we’ll tell you how) on a ticker.
While we use specific settings based on our backtesting results, you are welcome to adjust the parameters, such as changing the number of days outside the Bollinger bands, to better suit your trading preferences.
Example Trade 1: Lower Band Break
Let’s begin with an example where a ticker, say SPY, has closed below the lower Bollinger band for three consecutive days. Currently, SPY is trading at $504.45 after experiencing a few challenging days. Given its low volatility, it doesn’t take much for SPY to step out of its Bollinger bands. But, considering its resilience as a low-volatility asset, there’s a good chance it could bounce back from this dip.
Here’s a possible trade setup that you may find on our options screener:
- Buy a $516 call
- Sell two $517 calls
- Buy a $517.5 call.
Here is what your P&L profile will look like:
This structure limits your potential loss to $20 while setting up for a maximum gain of $80, translating to a 400% profit ratio. For this strategy to break even, SPY needs to climb above $516.20 by the expiration in 10 days, requiring just a +2.33% price movement. The maximum profit will occur at $517, but even if it climbs above $517.5, you’re still looking at a $30 gain (150% profit ratio).
To see how this might play out, you can run a backtest on SPY. Using our Colab file, make a copy for yourself and input “SPY” as your ticker. Leaving other parameters as they are is fine, but feel free to adjust them:
And here are your results:
Both in bull and bear market conditions, SPY has historically managed to recover within 10 days after dipping below the lower Bollinger band. In our current bull market scenario, SPY showed an average price increase of over 2% after 10 days.
Even in the bear market analysis, there was a recovery, though the gain tapered off towards the end of our observation period.
The Colab file will also indicate the number of similar past events, which in this case amounts to 13. Whether this is enough to base a trade on is your call. However, with a maximum loss of just $20, and considering how rare lower break events for SPY are, this setup might be too good to pass up for a trading idea.
Example Trade 2: Upper Band Break
Another example we want to show you involves a ticker that has closed above the upper Bollinger band for three days in a row. Consider ProShares UltraPro Short S&P500 (SPXU), which is currently trading at $36.38 after experiencing a bullish trend.
SPXU typically rises when the market faces downturns, and it gives us a good chance to test the mean-reverting nature of standard deviation, which is at the core of Bollinger bands. For this strategy to be effective, it’s crucial that no major macroeconomic or geopolitical events are currently stirring the markets, as such situations can make statistical analyses less reliable.
In this scenario, you could set up your trade as follows:
- Buy a $32.5 put
- Sell two $33.5 puts
- Buy a $35.5 put.
According to the profit and loss profile you see below, your maximum loss would be capped at $41.50, with the potential to make up to $158.50, which equates to a profit ratio of approximately 381.93%.
To break even, SPXU needs to drop below $35.09 within the next 10 days, a decrease of about -3.5%. While this might seem like a significant movement, it’s worth noting that SPXU was trading around $33 before its recent 3 bullish days, so we’re really not looking at a far-fetched scenario. The optimal profit will happen at a price of $33.5, and if SPXU falls below $32.5, you’re still looking at a gain of $58.50 (higher than the risk).
To further evaluate this opportunity, consider running a backtest on SPXU using our Colab file. After creating a copy for personal use, type “SPXU” in the “ticker” field. Again, you can change the other parameters if you’d like.
So, here is what you would obtain:
Focus on the green plot on the right. Historical data shows that SPXU generally declines after exceeding the upper Bollinger band, with an average price drop of around 5% in a bear scenario (i.e., the one experienced by SPXU at the moment).
Even the bull market case (on the left) showed a clear decline, although the downward price movement began to fade away by the end of our observation period. The Colab file will also indicate the number of similar past events (14). Again, it is up to you to decide whether this is a sufficient number of events to justify your trade, but keep in mind that your losses are relatively small in the worst-case scenario.
What Happens if a Backtest on a Ticker Shows No Promising Results?
However, not all backtests will yield straightforward results. Some stocks (for instance those with high volatility like the companies linked to the crypto sector), may not show movements out of the Bollinger bands as being as significant as they would for more stable, blue-chip stocks.
Take, for example, XLI, a ticker that appeared in our riskless up scan after it remained below the lower Bollinger band for 3 consecutive days. When we ran a backtest results for XLI, the outcome was less than promising, with a very large confidence interval indicated by the red area on the chart below:
This essentially means that the ticker’s price could move in either direction following a breakout from the bands, making it a challenging scenario for analysis.
In situations where a backtest does not present clear or promising results, our advice is twofold:
- It might be best to simply skip this trading opportunity. The market constantly presents new possibilities, and patience can lead to identifying opportunities with clearer outcomes in backtests.
- If curiosity prevails and you wish to further study a particular ticker, consider adjusting the number of days it spends outside the Bollinger bands in the predefined scan. Sometimes, you may find a more solid result when analyzing a different timeframe, although our observations suggest that this strategy typically performs best with stocks spending 2-3 days outside the bands. If this still holds, refer to the previous point and just wait for a better opportunity.
Run Your Own Backtest
If you liked the examples in our article and would like to try running your own backtests on different tickers, you can easily do so using our Google Colab notebook. Simply make a copy of the notebook and adjust the parameters as you see fit.
To quickly access all the resources you need, we added a few links below:
